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Define the job before comparing platforms
Write a short use-case brief before contacting vendors. Specify the task, the people who will use or be affected by the system, where it fits in the existing HR process, and what output it should produce. State what decision the output may influence and what would make the purchase worthwhile.
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Then decide whether AI is appropriate for that task at all. A broad aim such as improving recruiting is not testable; a bounded task, such as helping recruiters prioritize applications for human review, is. Do not assume that automation should make the final decision. The UK government’s Responsible AI in Recruitment guide, published 25 March 2024, recommends defining the system and its purpose and considering how it fits existing processes. NIST’s voluntary AI Risk Management Framework likewise begins by considering intended purpose, context, users, limitations, and deployment setting.
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Compare vendors against the same evidence
Use the same use case, deployment assumptions, evaluation population, and acceptance criteria for each platform. This makes differences in evidence and operational fit easier to see than comparing feature lists.
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| Evaluation area | What to examine | Evidence to request |
|---|---|---|
| Workflow fit | Coverage of the defined task and fit with current HR processes | A workflow walkthrough using your requirements, including exceptions |
| Performance and impact | Use-case-specific quality, outcomes, limitations, and failure levels | Validation details, agreed test results, and production references |
| Fairness and access | Potential disparate effects, accommodations, and routes to question outcomes | Testing conditions, known limits, accessibility details, and recourse process |
| Human control | What the system can do on its own, what needs approval, and how actions can be undone | Role and approval controls, logs, escalation steps, and reversal procedures |
| Data and governance | Required context, integrations, permissions, privacy, security, and jurisdictional fit | Architecture and data-handling documentation, plus change and incident procedures |
| Operations and exit | Ongoing effort, measurable value, monitoring, and safe retirement | Implementation plan, review measures, named owners, and exit arrangements |
Demand validation, not a persuasive demo
Ask vendors to substantiate claims about accuracy, validity, fairness, safety, impact, return on investment, and capability. Request relevant model and system documentation, impact and risk assessments, and a data protection impact assessment where applicable. A demo can show how a product behaves in a prepared scenario; it does not establish that its outputs are reliable under your conditions.
Before any vendor test, agree on the evaluation population, baseline, deployment-like conditions, measures, and acceptable failure levels. Include the cases that matter in your workflow, not just the easiest examples. Ask how results were measured, who selected the data, what was excluded, and what limitations apply. Validate vendor claims independently.
For evidence beyond a test, request references from organizations with comparable scale and workflows, and ask which outcomes were measured in production and how they were attributed to the AI function. Workday’s CHRO Buyer’s Guide to Agentic HR is vendor-authored; it recommends comparable-scale references and measurable outcomes rather than relying on demonstrations and pilots. Treat its guidance as a vendor perspective, not independent proof of a product’s results.
Test fairness, accessibility, and recourse across the workflow
In recruiting, potential effects can arise during sourcing, screening, interviewing, and selection—not only at the final decision. The UK government guide warns that unfair bias or discrimination may occur at each stage and identifies digital exclusion risks associated with factors such as age, disability, socioeconomic status, religion, or limited access to or proficiency with technology.
Ask which groups and conditions were tested, what the tests can and cannot establish, and how accommodations work in practice. Find out whether an applicant can request an alternative process, how a person reviews a consequential output, and how applicants can question or contest an outcome. Assess outcomes in the context where the tool will be used; a broad fairness statement does not tell you how the system performs with your jobs, applicants, and process.
Keep decisions accountable and actions controllable
Set clear human-AI roles for each step. Identify who owns the decision, who can review an output, and which actions the platform may take autonomously. Ask which actions require approval, what information the reviewer sees, what gets logged, and how staff can stop or reverse an action if it is wrong.
NIST’s AI Risk Management Framework is voluntary, not a law or certification. Its four functions—Govern, Map, Measure, and Manage—organize risk work, with governance running across the others. For a buyer, that means assigning accountable owners, documenting policies and risk tolerance, training relevant staff, maintaining an inventory of AI use, and scheduling reviews. A vendor’s controls can support this work, but they do not make the organization’s decisions accountable on its behalf.
Check data, permissions, and integration in your own environment
Establish what organizational context the use case needs and whether the platform can use it appropriately: for example, relevant HR records, role-based access, approval chains, and connections to existing systems. Review the actual architecture against your requirements rather than assuming that a particular deployment pattern is inherently safer or more suitable.
Workday argues that AI embedded in a system of record can draw on existing organizational structures and permissions, while separate layers can create data-pipeline and governance gaps. That is a vendor’s position, not a neutral guarantee. Ask any vendor to show how its design handles the requirements that apply to your environment, including data residency, access, retention, deletion, subprocessors, audit logs, incident handling, and the effect of product or model changes.
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Measure production value and the cost of operating the system
Agree on baseline measures and a review period before launch. For recruiting, possible measures to discuss include recruiter capacity, time to fill, hiring-manager review time, candidate engagement, and quality or fairness measures. Select measures that match the stated use case; do not treat an improvement claimed by a vendor or observed elsewhere as a forecast for your organization.
Include the work required to implement and run the system in the business case: integration, training, human oversight, monitoring, and change management, as well as the subscription. Ask for production evidence that includes exceptions and local policies, not only an idealized workflow. The purchase is worthwhile only if the results you can verify justify both the cost and the operational effort.
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Assign an owner for ongoing monitoring and define review frequency, performance and fairness measures, incident escalation, and triggers for reassessment. Re-evaluate after relevant changes to the model, data, product, or workflow. NIST states that AI risk management should be continuous and timely throughout the system lifecycle; its framework describes risk management as ongoing work rather than a one-time approval.
Best Value
Set in advance the conditions that would lead you to pause the system, conduct a new assessment, or retire it. Confirm how records and data will be handled if you leave the platform, and who is responsible for carrying out that process.
Apply the rules for each place and use case
The UK recruitment guide is UK government guidance and says it is not legal advice. The NIST framework is a voluntary, US-developed resource intended for flexible use. Neither is a jurisdiction-by-jurisdiction legal checklist. Before deployment, check current employment, privacy, accessibility, and AI requirements in every relevant jurisdiction and for the particular HR use case.
Make the selection on demonstrated fit
Choose the platform that best meets the defined workflow and can support its claims with relevant evidence, workable safeguards, accountable controls, and a credible operating and exit plan. If a vendor cannot provide evidence or explain how the tool behaves in your conditions, treat that as an unresolved risk—not as proof that the system is ready for consequential HR use.
Quick Recap
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